This disclosure relates to the combination of data from multiple sources, for example sensors.
Conventional techniques for this comprise well-known filters, such as a complementary filter. These have been used in Inertial Measurement Units (“IMU”) to combine, for example, accelerometer and gyroscope data. Such filters only use a single estimate as a baulked error, and do not generally comprise, for example, a feedback loop.
More complex systems are known, for example a Kalman filter, which involve estimating state variables as well as their uncertainty, and continuously updating these using a weighted average. These methods typically involve feedback loops as well as complex matrix calculations.
It is desired to provide improved methods of combining data from multiple sensors and to reduce measurement error.